Predicting Seasonal Deformation Using InSAR and Machine Learning in the Permafrost Regions of the Yangtze River Source Region

Predicting Seasonal Deformation Using InSAR and Machine Learning in the Permafrost Regions of the Yangtze River Source Region

Abstract Quantifying seasonal deformation is essential for accurately determining the thickness of the active layer and the distribution of water content within it, providing insights into the freeze‐thaw dynamics of permafrost environments and their sensitivity to climate change. Due to the limited...

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Bibliographic Details
Main Authors: Jie Chen, Xingchen Lin, Tonghua Wu, Junming Hao, Xiaodong Wu, Defu Zou, Xiaofan Zhu, Guojie Hu, Yongping Qiao, Dong Wang, Sizhong Yang, Lina Zhang
Format: Article
Language:English
Published: Wiley 2024-09-01
Series:Water Resources Research
Subjects:
seasonal deformation
InSAR
permafrost
machine learning
active layer
Qinghai‐Tibet Plateau
Online Access:https://doi.org/10.1029/2023WR036700
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https://doi.org/10.1029/2023WR036700

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